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Ucla Pso Manual

USER’S MANUAL of UCLA-PSO ALGORITHM (MATLAB VERSION) Nanbo Jin and Yahya Rahmat-Samii Department of Electrical Engineering University of California, Los Angeles http://www.ee.ucla.edu/antlab April 2007 Chapter 1 1.1 RPSO Program and Subroutines Figure 1.1: A flowchart of subroutines in the RPSO algorithm. 1 Figure 1.1 shows the flowchart of subroutines in the RPSO algorithm. Before starting an optimization, the optimizer is configured by specifying input parameters

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  USER’S MANUALof UCLA-PSO ALGORITHM (MATLAB VERSION)Nanbo Jin and Yahya Rahmat-Samii Department of Electrical EngineeringUniversity of California, Los Angeleshttp://www.ee.ucla.edu/antlabApril 2007  Chapter 1 1.1 RPSO Program and Subroutines Figure 1.1: A flowchart of subroutines in the RPSO algorithm. 1  Figure 1.1 shows the flowchart of subroutines in the RPSO algorithm. Before starting anoptimization, the optimizer is configured by specifying input parameters in init global.m .The subroutine PSO.m contains the main loop that updates V , X , P and G iteratively. Inthis loop, boundary conditions are implemented by bc fit.m and the evaluator is called by evalfit.m . The main loop is executed for the number of iterations specified in init global.m .The position and fitness values of all candidate designs encountered in the optimization isrecorded in an output file, and the subroutine convg.m is applied for post-processing. 1.2 Program Initialization The input parameters need to be specified in init global.m are:1. NUM AGENTS : The number of agents in a swarm, M  .2. NUMDIM : The dimension of solution space, N  . It equals to the number of unknowns in avariable to be optimized.3. MAX ITER : The number of iterations to execute the optimization.4. SEED : The optimization is executed without using a seed (a randomly initialized searm)if  SEED = 0. The seed specified in seed.mat is used if  SEED = 1.5. VMAX : The fractional maximum velocity of each agent. Its value is typically selectedbetween 0.1 and 0.2.6. MAX WEIGHT, MIN WEIGHT : The upper and lower limits of the time varying inertia weight.Their values are typically selected as 0.9 and 0.4, respectively.7. C1, C2 : Hooke’s coefficients for modelling attractive forces from um  >> timecost = · · · File convg.m is a post-processing subroutine that plots the convergence curve and necessaryproperties of the global optimum. In convg.m , the command lines: load xxxx.txt;c = xxxx(1:NUM AGENTS*MAX ITER, :); should be modified according to the output file name ‘xxxx’ . The post-processing is exe-cuted by typing: >> convg in the command window, with the global optimum stored in a variable best design . 1.4 Example: 10-element Low SLL Aperiodic Array :); timecost =inthecommavari >> timecost =inq11j12 10.9098.817.12 10(v)Tj50 0 8.333BT/R486v